Visualise

# libraries
library(here)
library(readr)
library(DT)

# variables
url_ac <- "https://oceanview.pfeg.noaa.gov/erddap/tabledap/cciea_AC.csv"
# if ERDDAP server down (Error in download.file) with URL above, use this:
#    url_ac <- "https://raw.githubusercontent.com/noaa-iea/r3-train/master/data/cciea_AC.csv"
csv_ac <- here("data/cciea_AC.csv")

# download data
if (!file.exists(csv_ac))
  download.file(url_ac, csv_ac)

# read data
d_ac        <- read_csv(csv_ac, col_names = F, skip = 2)

## 
## -- Column specification -------------------------------------------------------------
## cols(
##   .default = col_double(),
##   X1 = col_datetime(format = "")
## )
## i Use `spec()` for the full column specifications.

names(d_ac) <- names(read_csv(csv_ac))

## 
## -- Column specification -------------------------------------------------------------
## cols(
##   .default = col_double(),
##   time = col_datetime(format = "")
## )
## i Use `spec()` for the full column specifications.

# show data
datatable(d_ac)

library(dplyr)
library(ggplot2)

# subset data
d_coast <- d_ac %>% 
  # select columns
  select(time, total_fisheries_revenue_coastwide) %>% 
  # filter rows
  filter(!is.na(total_fisheries_revenue_coastwide))
datatable(d_coast)

# ggplot object
p_coast <- d_coast %>% 
  # setup aesthetics
  ggplot(aes(x = time, y = total_fisheries_revenue_coastwide)) +
  # add geometry
  geom_line()

# show plot
p_coast

p_coast + 
  geom_smooth(method = "lm")

## `geom_smooth()` using formula 'y ~ x'

d_coast %>% 
  # setup aesthetics
  ggplot(aes(x = total_fisheries_revenue_coastwide)) +
  # add geometry
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

library(stringr)
library(tidyr)

d_rgn <- d_ac %>% 
  # select columns
  select(
    time, 
    starts_with("total_fisheries_revenue")) %>% 
  # exclude column
  select(-total_fisheries_revenue_coastwide) %>% 
  # pivot longer
  pivot_longer(-time) %>% 
  # mutate region by stripping other
  mutate(
    region = name %>% 
      str_replace("total_fisheries_revenue_", "") %>% 
      str_to_upper()) %>% 
  # filter for not NA
  filter(!is.na(value)) %>% 
  # select columns
  select(time, region, value)
  
# create plot object
p_rgn <- ggplot(
  d_rgn,
  # aesthetics
  aes(
    x     = time, 
    y     = value,
    group = region,
    color = region)) +
  # geometry
  geom_line()

# show plot
p_rgn

p_rgn <- p_rgn +
  labs(
    title  = "Fisheries Revenue",
    x      = "Year",
    y      = "Millions $ (year 2015)",
    color  = "Region")
p_rgn

p_rgn +
  facet_wrap(vars(region))

library(glue)
library(lubridate)

yr_max <- year(max(d_rgn$time))

d_rgn %>% 
  # filter by most recent time
  filter(year(time) == yr_max) %>% 
  # setup aesthetics
  ggplot(aes(x = region, y = value, fill = region)) +
  # add geometry
  geom_col() +
  # add labels
  labs(
    title  = glue("Fisheries Revenue for {yr_max}"),
    x      = "Region",
    y      = "Millions $ (year 2015)",
    fill   = "Region")

d_rgn %>% 
  # setup aesthetics
  ggplot(aes(x = region, y = value, fill = region)) +
  # add geometry
  geom_boxplot() +
  # add labels
  labs(
    title  = "Fisheries Revenue Variability",
    x      = "Region",
    y      = "Millions $ (year 2015)") +
  # drop legend since redundant with x axis
  theme(
    legend.position = "none")

p_rgn_violin <- d_rgn %>% 
  # setup aesthetics
  ggplot(aes(x = region, y = value, fill = region)) +
  # add geometry
  geom_violin() +
  # add labels
  labs(
    title  = "Fisheries Revenue Variability",
    x      = "Region",
    y      = "Millions $ (year 2015)") +
  # drop legend since redundant with x axis
  theme(
    legend.position = "none")
p_rgn_violin

p_rgn_violin +
  theme_classic()

plotly::ggplotly(p_rgn)

library(dygraphs)

d_rgn_wide <- d_rgn %>% 
  mutate(
    Year = year(time)) %>% 
  select(Year, region, value) %>% 
  pivot_wider(
    names_from  = region,
    values_from = value)
datatable(d_rgn_wide)

d_rgn_wide %>% 
  dygraph() %>% 
  dyRangeSelector()